
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	DDIMPipeline
	_A								=	UNCONDITIONAL_IMAGE_GENERATION_PARAMS
	_A								=	PipelineTesterMixin.required_optional_params - {
	    'num_images_per_prompt',
	    'latents',
	    'callback',
	    'callback_steps',
	}
	_A								=	UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
	_A								=	False







	def   _lowerCamelCase							(   self						:int		)						->					List[str]:
								torch.manual_seed(0		)
								__UpperCamelCase      :  int       =   UNetaDModel(
								    block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  sample_size=3_2	,  in_channels=3	,  out_channels=3	,  down_block_types=("DownBlock2D", "AttnDownBlock2D")	,  up_block_types=("AttnUpBlock2D", "UpBlock2D")	,  )
								__UpperCamelCase      :  List[Any]       =   DDIMScheduler()
								__UpperCamelCase      :  int       =   {"unet": unet, "scheduler": scheduler}
								return components







	def   _lowerCamelCase							(   self						:int	,  a						:List[Any]	,  a						:List[Any]=0		)						->					int:
								if str(a		).startswith("mps"		):
															__UpperCamelCase      :  Optional[Any]       =   torch.manual_seed(a		)
								else:
															__UpperCamelCase      :  Tuple       =   torch.Generator(device=a		).manual_seed(a		)
								__UpperCamelCase      :  str       =   {
								    "batch_size": 1,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}
								return inputs







	def   _lowerCamelCase							(   self						:List[Any]		)						->					str:
								__UpperCamelCase      :  str       =   "cpu"

								__UpperCamelCase      :  int       =   self.get_dummy_components()
								__UpperCamelCase      :  Dict       =   self.pipeline_class(**a		)
								pipe.to(a		)
								pipe.set_progress_bar_config(disable=a		)

								__UpperCamelCase      :  str       =   self.get_dummy_inputs(a		)
								__UpperCamelCase      :  str       =   pipe(**a		).images
								__UpperCamelCase      :  Dict       =   image[0, -3:, -3:, -1]

								self.assertEqual(image.shape	,  (1, 3_2, 3_2, 3)		)
								__UpperCamelCase      :  Union[str, Any]       =   np.array(
								    [1.0_0_0E0_0, 5.7_1_7E-0_1, 4.7_1_7E-0_1, 1.0_0_0E0_0, 0.0_0_0E0_0, 1.0_0_0E0_0, 3.0_0_0E-0_4, 0.0_0_0E0_0, 9.0_0_0E-0_4]		)
								__UpperCamelCase      :  List[Any]       =   np.abs(image_slice.flatten() - expected_slice		).max()
								self.assertLessEqual(a	,  1E-3		)







	def   _lowerCamelCase							(   self						:str		)						->					str:
								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)







	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					str:
								super().test_save_load_local(expected_max_difference=3E-3		)







	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Tuple:
								super().test_save_load_optional_components(expected_max_difference=3E-3		)







	def   _lowerCamelCase							(   self						:Any		)						->					List[str]:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3		)




@slow
@require_torch_gpu
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Dict		)						->					Dict:
								__UpperCamelCase      :  Tuple       =   "google/ddpm-cifar10-32"

								__UpperCamelCase      :  Dict       =   UNetaDModel.from_pretrained(a		)
								__UpperCamelCase      :  List[Any]       =   DDIMScheduler()

								__UpperCamelCase      :  Union[str, Any]       =   DDIMPipeline(unet=a	,  scheduler=a		)
								ddim.to(a		)
								ddim.set_progress_bar_config(disable=a		)

								__UpperCamelCase      :  Optional[Any]       =   torch.manual_seed(0		)
								__UpperCamelCase      :  Optional[int]       =   ddim(generator=a	,  eta=0.0	,  output_type="numpy"		).images

								__UpperCamelCase      :  int       =   image[0, -3:, -3:, -1]

								assert image.shape == (1, 3_2, 3_2, 3)
								__UpperCamelCase      :  Optional[int]       =   np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Optional[Any]:
								__UpperCamelCase      :  Optional[Any]       =   "google/ddpm-ema-bedroom-256"

								__UpperCamelCase      :  Dict       =   UNetaDModel.from_pretrained(a		)
								__UpperCamelCase      :  List[str]       =   DDIMScheduler.from_pretrained(a		)

								__UpperCamelCase      :  Union[str, Any]       =   DDIMPipeline(unet=a	,  scheduler=a		)
								ddpm.to(a		)
								ddpm.set_progress_bar_config(disable=a		)

								__UpperCamelCase      :  Optional[Any]       =   torch.manual_seed(0		)
								__UpperCamelCase      :  Union[str, Any]       =   ddpm(generator=a	,  output_type="numpy"		).images

								__UpperCamelCase      :  Optional[Any]       =   image[0, -3:, -3:, -1]

								assert image.shape == (1, 2_5_6, 2_5_6, 3)
								__UpperCamelCase      :  Optional[int]       =   np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2

import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
		from transformers import MarkupLMFeatureExtractor




class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def __init__(   self						:Optional[int]	,  a						:List[str]		)						->					Union[str, Any]:
								__UpperCamelCase      :  Dict       =   parent







	def   _lowerCamelCase							(   self						:Dict		)						->					int:
								return {}




def       _SCREAMING_SNAKE_CASE   (						)   ->       Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   "<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR=\"FFFFFF\">\n    <HR>\n    <a href=\"http://google.com\">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style=\"color:#0000FF\">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>"

							__UpperCamelCase      :  Optional[int]       =   "\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    "

							return [html_string_a, html_string_a]




@require_bsa
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	MarkupLMFeatureExtractor if is_bsa_available() else None







	def   _lowerCamelCase							(   self						:List[str]		)						->					str:
								__UpperCamelCase      :  List[str]       =   MarkupLMFeatureExtractionTester(self		)







	@property
	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[str]:
								return self.feature_extract_tester.prepare_feat_extract_dict()







	def   _lowerCamelCase							(   self						:Dict		)						->					Dict:
								# Initialize feature_extractor
								__UpperCamelCase      :  Tuple       =   self.feature_extraction_class()

								# Test not batched input
								__UpperCamelCase      :  Union[str, Any]       =   get_html_strings()[0]
								__UpperCamelCase      :  Any       =   feature_extractor(a		)

								# fmt: off
								__UpperCamelCase      :  Union[str, Any]       =   [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
								__UpperCamelCase      :  Tuple       =   [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]]
								# fmt: on

								self.assertEqual(encoding.nodes	,  a		)
								self.assertEqual(encoding.xpaths	,  a		)

								# Test batched
								__UpperCamelCase      :  str       =   get_html_strings()
								__UpperCamelCase      :  List[Any]       =   feature_extractor(a		)

								# fmt: off
								__UpperCamelCase      :  Optional[int]       =   expected_nodes + [["My First Heading", "My first paragraph."]]
								__UpperCamelCase      :  Tuple       =   expected_xpaths + [["/html/body/h1", "/html/body/p"]]

								self.assertEqual(len(encoding.nodes		)	,  2		)
								self.assertEqual(len(encoding.xpaths		)	,  2		)

								self.assertEqual(encoding.nodes	,  a		)
								self.assertEqual(encoding.xpaths	,  a		)